Search Results for author:
Found 31 papers, 18 papers with code
Surrogate Learning in Meta-Black-Box Optimization: A Preliminary Study
Recent Meta-Black-Box Optimization (MetaBBO) approaches have shown possibility of enhancing the optimization performance through learning meta-level policies to dynamically configure low-level optimizers.
Feedback-Free Resource Scheduling: Towards Flexible Multi-BS Cooperation in FD-RAN
Flexible cooperation among base stations (BSs) is critical to improve resource utilization efficiency and meet personalized user demands.
ConfigX: Modular Configuration for Evolutionary Algorithms via Multitask Reinforcement Learning
To address this limitation, we introduce ConfigX, a new paradigm of the MetaBBO framework that is capable of learning a universal configuration agent (model) for boosting diverse EAs.
Electromagnetic Scattering Kernel Guided Reciprocal Point Learning for SAR Open-Set Recognition
This enhances the ability to extract intrinsic non-linear features and specific scattering characteristics in SAR images, thereby improving the discriminative features of the model and mitigating the impact of imaging variations on classification performance.
MEGA-Bench: Scaling Multimodal Evaluation to over 500 Real-World Tasks
We evaluate a wide variety of frontier vision-language models on MEGA-Bench to understand their capabilities across these dimensions.
Neural Exploratory Landscape Analysis for Meta-Black-Box-Optimization
Recent research in Meta-Black-Box Optimization (MetaBBO) have shown that meta-trained neural networks can effectively guide the design of black-box optimizers, significantly reducing the need for expert tuning and delivering robust performance across complex problem distributions.
PuzzleFusion++: Auto-agglomerative 3D Fracture Assembly by Denoise and Verify
This paper proposes a novel "auto-agglomerative" 3D fracture assembly method, PuzzleFusion++, resembling how humans solve challenging spatial puzzles.
Performance Analysis of Uplink/Downlink Decoupled Access in Cellular-V2X Networks
This paper firstly develops an analytical framework to investigate the performance of uplink (UL) / downlink (DL) decoupled access in cellular vehicle-to-everything (C-V2X) networks, in which a vehicle's UL/DL can be connected to different macro/small base stations (MBSs/SBSs) separately.
Auto-configuring Exploration-Exploitation Tradeoff in Evolutionary Computation via Deep Reinforcement Learning
Evolutionary computation (EC) algorithms, renowned as powerful black-box optimizers, leverage a group of individuals to cooperatively search for the optimum.
MapTracker: Tracking with Strided Memory Fusion for Consistent Vector HD Mapping
The paper further makes benchmark contributions by 1) Improving processing code for existing datasets to produce consistent ground truth with temporal alignments and 2) Augmenting existing mAP metrics with consistency checks.
Deep Reinforcement Learning for Dynamic Algorithm Selection: A Proof-of-Principle Study on Differential Evolution
As a proof-of-principle study, we apply this framework to a group of Differential Evolution algorithms.
LLaMoCo: Instruction Tuning of Large Language Models for Optimization Code Generation
Recent research explores optimization using large language models (LLMs) by either iteratively seeking next-step solutions from LLMs or directly prompting LLMs for an optimizer.
MVDiffusion++: A Dense High-resolution Multi-view Diffusion Model for Single or Sparse-view 3D Object Reconstruction
MVDiffusion++ achieves superior flexibility and scalability with two surprisingly simple ideas: 1) A ``pose-free architecture'' where standard self-attention among 2D latent features learns 3D consistency across an arbitrary number of conditional and generation views without explicitly using camera pose information; and 2) A ``view dropout strategy'' that discards a substantial number of output views during training, which reduces the training-time memory footprint and enables dense and high-resolution view synthesis at test time.
Symbol: Generating Flexible Black-Box Optimizers through Symbolic Equation Learning
Recent Meta-learning for Black-Box Optimization (MetaBBO) methods harness neural networks to meta-learn configurations of traditional black-box optimizers.
Toward a Reinforcement-Learning-Based System for Adjusting Medication to Minimize Speech Disfluency
We propose a reinforcement learning (RL)-based system that would automatically prescribe a hypothetical patient medication that may help the patient with their mental health-related speech disfluency, and adjust the medication and the dosages in response to zero-cost frequent measurement of the fluency of the patient.
Channel-Feedback-Free Transmission for Downlink FD-RAN: A Radio Map based Complex-valued Precoding Network Approach
As the demand for high-quality services proliferates, an innovative network architecture, the fully-decoupled RAN (FD-RAN), has emerged for more flexible spectrum resource utilization and lower network costs.
MetaBox: A Benchmark Platform for Meta-Black-Box Optimization with Reinforcement Learning
To fill this gap, we introduce MetaBox, the first benchmark platform expressly tailored for developing and evaluating MetaBBO-RL methods.
Improving Pixel-based MIM by Reducing Wasted Modeling Capability
There has been significant progress in Masked Image Modeling (MIM).
MVDiffusion: Enabling Holistic Multi-view Image Generation with Correspondence-Aware Diffusion
This paper introduces MVDiffusion, a simple yet effective method for generating consistent multi-view images from text prompts given pixel-to-pixel correspondences (e. g., perspective crops from a panorama or multi-view images given depth maps and poses).
PolyDiffuse: Polygonal Shape Reconstruction via Guided Set Diffusion Models
This paper presents PolyDiffuse, a novel structured reconstruction algorithm that transforms visual sensor data into polygonal shapes with Diffusion Models (DM), an emerging machinery amid exploding generative AI, while formulating reconstruction as a generation process conditioned on sensor data.
PixMIM: Rethinking Pixel Reconstruction in Masked Image Modeling
Masked Image Modeling (MIM) has achieved promising progress with the advent of Masked Autoencoders (MAE) and BEiT.
MoQuad: Motion-focused Quadruple Construction for Video Contrastive Learning
Learning effective motion features is an essential pursuit of video representation learning.
HEAT: Holistic Edge Attention Transformer for Structured Reconstruction
This paper presents a novel attention-based neural network for structured reconstruction, which takes a 2D raster image as an input and reconstructs a planar graph depicting an underlying geometric structure.
Edge Classification
Extracting Buildings In Remote Sensing Images
+1
APANet: Adaptive Prototypes Alignment Network for Few-Shot Semantic Segmentation
In practice, it can adaptively generate multiple class-agnostic prototypes for query images and learn feature alignment in a self-contrastive manner.
Ranked #54 on
Few-Shot Semantic Segmentation
on COCO-20i (1-shot)
Adaptive Appearance Rendering
Hence, we develop an approach based on intermediate representations of poses and appearance: our pose-guided appearance rendering network firstly encodes the targets' poses using an encoder-decoder neural network.
SCNet: Enhancing Few-Shot Semantic Segmentation by Self-Contrastive Background Prototypes
To this end, we generate self-contrastive background prototypes directly from the query image, with which we enable the construction of complete sample pairs and thus a complementary and auxiliary segmentation task to achieve the training of a better segmentation model.
Learning the Best Pooling Strategy for Visual Semantic Embedding
Visual Semantic Embedding (VSE) is a dominant approach for vision-language retrieval, which aims at learning a deep embedding space such that visual data are embedded close to their semantic text labels or descriptions.
BabyWalk: Going Farther in Vision-and-Language Navigation by Taking Baby Steps
To this end, we propose BabyWalk, a new VLN agent that is learned to navigate by decomposing long instructions into shorter ones (BabySteps) and completing them sequentially.
Floor-SP: Inverse CAD for Floorplans by Sequential Room-wise Shortest Path
This paper proposes a new approach for automated floorplan reconstruction from RGBD scans, a major milestone in indoor mapping research.
Probabilistic Neural Programmed Networks for Scene Generation
In this paper we address the text to scene image generation problem.
Learning to Forecast Videos of Human Activity with Multi-granularity Models and Adaptive Rendering
An architecture combining a hierarchical temporal model for predicting human poses and encoder-decoder convolutional neural networks for rendering target appearances is proposed.
